Insects like honeybees exhibit remarkable navigational abilities despite their simple nervous systems, showcasing expertise in tasks such as long-distance travel, landmark recognition, and spatial memory. These skills are crucial for efficient foraging and homing. In robotics, one of the main challenges is to navigate in GPS-denied environments with limited sensors and processors onboard. In this study, we propose a novel navigation strategy that uses learning flights during which a robot directly maps images to nest location vectors, inspired by honeybees. In our previous research, the learning phase was modeled using compact convolutional neural networks (CNNs) and demonstrated successful learning and control in simulation. In this work, we combine this homing model with odometry and implement both in a real-world quadrotor. More specifically, we examine how this model can compensate for the drone’s odometric drift to reach home after it performs a long-distance outbound flight. Real-world experiments demonstrate the proof of concept of the proposed navigation strategy in both indoor and outdoor flights.

Drones have been an emerging trend in the last few years. They are used in multiple industries already, from photography and videography to racing. More use cases are now being conceived, such as using drones to deliver packages and food to people at home, using drones for inspections, or even using them as rescue searching vehicles in hostile environments. Even more possibilities open up once the drones bundle their forces to create swarms. The lifting capabilities of drones are still somewhat limited, but in a swarm they might be able to lift heavy payloads. This report covers the design of a concept of a payload carrying swarm, intended to lift cargo up to 500 kg to even the top of a tall building.